Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	115,571
2	South Dakota	103,264
3	Iowa	81,425
4	Wisconsin	80,942
5	Nebraska	77,346
6	Utah	73,573
7	Rhode Island	69,920
8	Wyoming	68,724
9	Idaho	68,648
10	Montana	68,635
11	Minnesota	67,717
12	Illinois	67,698
13	Kansas	66,284
14	Tennessee	66,256
15	Indiana	64,353
16	Arkansas	61,984
17	Nevada	61,621
18	Mississippi	60,848
19	Alabama	60,755
20	Oklahoma	60,593
21	Missouri	59,880
22	Louisiana	58,002
23	New Mexico	57,848
24	Arizona	57,765
25	Alaska	56,541
26	Florida	52,816
27	Texas	51,519
28	Kentucky	51,182
29	Colorado	50,643
30	Georgia	49,650
31	South Carolina	49,483
32	Ohio	48,814
33	Delaware	47,708
34	Michigan	47,278
35	New Jersey	45,857
36	Connecticut	43,192
37	Massachusetts	42,688
38	North Carolina	42,116
39	California	41,021
40	New York	40,600
41	Pennsylvania	39,479
42	Maryland	39,237
43	West Virginia	35,931
44	District of Columbia	35,477
45	Virginia	33,407
46	Puerto Rico	30,247
47	Washington	27,885
48	New Hampshire	23,442
49	Oregon	22,533
50	Hawaii	13,764
51	Maine	12,162
52	Vermont	9,386

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Tennessee	1,294
2	Rhode Island	1,023
3	Arizona	1,021
4	Indiana	905
5	Delaware	903
6	Oklahoma	877
7	Nevada	853
8	Ohio	809
9	Utah	803
10	California	781
11	Pennsylvania	770
12	New Mexico	752
13	Alaska	724
14	West Virginia	698
15	Connecticut	676
16	Missouri	671
17	Massachusetts	667
18	Mississippi	651
19	Minnesota	642
20	South Dakota	633
21	Alabama	620
22	South Carolina	618
23	Illinois	610
24	Arkansas	600
25	New Hampshire	592
26	Kentucky	579
27	New Jersey	570
28	Wyoming	570
29	Wisconsin	567
30	North Carolina	531
31	Kansas	514
32	New York	514
33	Idaho	512
34	Colorado	467
35	Maryland	464
36	Georgia	453
37	Nebraska	445
38	Montana	444
39	Florida	434
40	Virginia	418
41	Michigan	412
42	Louisiana	390
43	North Dakota	373
44	Texas	366
45	Iowa	347
46	Washington	343
47	District of Columbia	321
48	Oregon	285
49	Maine	283
50	Puerto Rico	206
51	Vermont	168
52	Hawaii	108

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,001
2	New York	1,813
3	Massachusetts	1,652
4	Connecticut	1,526
5	North Dakota	1,526
6	Louisiana	1,472
7	Rhode Island	1,467
8	South Dakota	1,423
9	Mississippi	1,412
10	Illinois	1,219
11	Michigan	1,129
12	Iowa	1,036
13	Indiana	1,016
14	District of Columbia	1,014
15	Arizona	1,010
16	Arkansas	990
17	Pennsylvania	989
18	New Mexico	943
19	Florida	931
20	Georgia	923
21	South Carolina	922
22	Maryland	850
23	Texas	847
24	Delaware	837
25	Alabama	836
26	Nevada	830
27	Tennessee	803
28	Minnesota	801
29	Missouri	779
30	Montana	765
31	Wisconsin	748
32	Nebraska	740
33	Kansas	723
34	Colorado	697
35	Idaho	670
36	Ohio	645
37	North Carolina	562
38	Wyoming	554
39	Kentucky	553
40	West Virginia	545
41	California	536
42	Oklahoma	523
43	Virginia	517
44	New Hampshire	444
45	Washington	404
46	Puerto Rico	401
47	Utah	332
48	Oregon	276
49	Alaska	231
50	Hawaii	192
51	Maine	192
52	Vermont	153

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	South Dakota	18
2	New Mexico	14
3	Rhode Island	14
4	Arkansas	12
5	Nebraska	11
6	North Dakota	11
7	Illinois	10
8	Michigan	10
9	Minnesota	10
10	Tennessee	10
11	Colorado	9
12	Pennsylvania	9
13	Alaska	8
14	Indiana	8
15	Iowa	8
16	Mississippi	8
17	Connecticut	7
18	Nevada	7
19	West Virginia	7
20	Massachusetts	6
21	Idaho	5
22	Louisiana	5
23	New York	5
24	Oklahoma	5
25	South Carolina	5
26	Arizona	4
27	Florida	4
28	Kansas	4
29	Kentucky	4
30	Maryland	4
31	Missouri	4
32	Montana	4
33	New Jersey	4
34	Texas	4
35	Wisconsin	4
36	Delaware	3
37	District of Columbia	3
38	New Hampshire	3
39	North Carolina	3
40	Ohio	3
41	Puerto Rico	3
42	Utah	3
43	California	2
44	Georgia	2
45	Maine	2
46	Alabama	1
47	Hawaii	1
48	Oregon	1
49	Vermont	1
50	Virginia	1
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	235,935	1	99
Norton	Kansas	212,833	2	99
Bon Homme	South Dakota	206,492	3	99
Lincoln	Arkansas	204,929	4	99
Buffalo	South Dakota	204,383	5	99
Davidson	Tennessee	80,276	469	85
Richland	South Carolina	55,395	1439	54
York	South Carolina	43,519	2098	33
Orange	California	34,030	2523	19
Pierce	Washington	25,823	2782	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Foster	North Dakota	5,919	4	99
Gregory	South Dakota	5,735	5	99
Richland	South Carolina	755	1585	49
Davidson	Tennessee	693	1725	45
York	South Carolina	559	1983	36
Orange	California	533	2046	34
Pierce	Washington	324	2511	20

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.